Coverage for tests/test_RBTransiNetInterface.py: 32%

1# This file is part of meas_transiNet. 

2# 

3# Developed for the LSST Data Management System. 

4# This product includes software developed by the LSST Project 

5# (https://www.lsst.org). 

6# See the COPYRIGHT file at the top-level directory of this distribution 

7# for details of code ownership. 

8# 

9# This program is free software: you can redistribute it and/or modify 

10# it under the terms of the GNU General Public License as published by 

11# the Free Software Foundation, either version 3 of the License, or 

12# (at your option) any later version. 

13# 

14# This program is distributed in the hope that it will be useful, 

15# but WITHOUT ANY WARRANTY; without even the implied warranty of 

16# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 

17# GNU General Public License for more details. 

18# 

19# You should have received a copy of the GNU General Public License 

20# along with this program. If not, see <https://www.gnu.org/licenses/>. 

21 

22import unittest 

23 

24import numpy as np 

25 

26from lsst.meas.transiNet import RBTransiNetTask 

27from lsst.meas.transiNet import RBTransiNetInterface, CutoutInputs 

28 

29 

30class TestInference(unittest.TestCase): 

31 def setUp(self): 

32 

33 # Create a mock TransiNetTask. 

34 config = RBTransiNetTask.ConfigClass() 

35 config.modelPackageName = "dummy" 

36 config.modelPackageStorageMode = "local" 

37 self.task = RBTransiNetTask(config=config) 

38 self.interface = RBTransiNetInterface(self.task) 

39 

40 def test_infer_single_empty(self): 

41 """Test running infer on a single blank triplet. 

42 """ 

43 data = np.zeros((256, 256), dtype=np.single) 

44 inputs = CutoutInputs(science=data, difference=data, template=data) 

45 result = self.interface.infer([inputs]) 

46 self.assertTupleEqual(result.shape, (1,)) 

47 self.assertAlmostEqual(result[0], 0.5011908) # Empricial meaningless value spit by this very model 

48 

49 def test_infer_many(self): 

50 """Test running infer on a large number of images, 

51 to make sure partitioning to batches works. 

52 """ 

53 data = np.zeros((256, 256), dtype=np.single) 

54 inputs = [CutoutInputs(science=data, difference=data, template=data) for _ in range(100)] 

55 result = self.interface.infer(inputs) 

56 self.assertTupleEqual(result.shape, (100,)) 

57 self.assertAlmostEqual(result[0], 0.5011908)